Feature engineering for electricity load forecasting

The purpose of this notebook is to demonstrate how to use skrub and polars to perform feature engineering for electricity load forecasting.

We will build a set of features from different sources:

• Historical weather data for 10 medium to large urban areas in France;

• Holidays and calendar features for France;

• Historical electricity load data for the whole of France.

All these data sources cover a time range from March 23, 2021 to May 31, 2025.

Since our maximum forecasting horizon is 24 hours, we consider that the future weather data is known at a chosen prediction time. Similarly, the holidays and calendar features are known at prediction time for any point in the future.

Therefore, features derived from the weather and calendar data can be used to engineer “future covariates”. Since the load data is our prediction target, we will can also use it to engineer “past covariates” such as lagged features and rolling aggregations.

Environment setup

We need to install some extra dependencies for this notebook if needed (when running jupyterlite). We need the development version of skrub to be able to use the skrub expressions.

%pip install -q https://pypi.anaconda.org/ogrisel/simple/polars/1.24.0/polars-1.24.0-cp39-abi3-emscripten_3_1_58_wasm32.whl
%pip install -q altair holidays https://pypi.anaconda.org/ogrisel/simple/skrub/0.6.dev0/skrub-0.6.dev0-py3-none-any.whl

ERROR: polars-1.24.0-cp39-abi3-emscripten_3_1_58_wasm32.whl is not a supported wheel on this platform.

Note: you may need to restart the kernel to use updated packages.

Note: you may need to restart the kernel to use updated packages.

# The following 3 imports are only needed to workaround some limitations
# when using polars in a pyodide/jupyterlite notebook.
import tzdata  # noqa: F401
import pandas as pd
from pyarrow.parquet import read_table

import polars as pl
import skrub
from pathlib import Path
import holidays
import warnings

# Ignore warnings from pkg_resources triggered by Python 3.13's multiprocessing.
warnings.filterwarnings("ignore", category=UserWarning, module="pkg_resources")

Time range

Let’s define a hourly time range from March 23, 2021 to May 31, 2025 that will be used to join the electricity load data and the weather data. The time range is in UTC timezone to avoid any ambiguity when joining with the weather data that is also in UTC.

We wrap the polars dataframe in a skrub variable to benefit from the built-in TableReport display in the notebook. Using the skrub expression system will also be useful later.

time_range_start = pl.datetime(2021, 3, 23, hour=0, time_zone="UTC")
time_range_end = pl.datetime(2025, 5, 31, hour=23, time_zone="UTC")
time = skrub.var(
    "time",
    pl.DataFrame().with_columns(
        pl.datetime_range(
            start=time_range_start,
            end=time_range_end,
            time_zone="UTC",
            interval="1h",
        ).alias("time"),
    ),
)
time

<Var 'time'>

Show graph

Result:

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To avoid network issues when running this notebook, the necessary data files have already been downloaded and saved in the datasets folder. See the README.md file for instructions to download the data manually if you want to re-run this notebook with more recent data.

data_source_folder = Path("../datasets")
for data_file in sorted(data_source_folder.iterdir()):
    print(data_file)

../datasets/README.md
../datasets/Total Load - Day Ahead _ Actual_202101010000-202201010000.csv
../datasets/Total Load - Day Ahead _ Actual_202201010000-202301010000.csv
../datasets/Total Load - Day Ahead _ Actual_202301010000-202401010000.csv
../datasets/Total Load - Day Ahead _ Actual_202401010000-202501010000.csv
../datasets/Total Load - Day Ahead _ Actual_202501010000-202601010000.csv
../datasets/weather_bayonne.parquet
../datasets/weather_brest.parquet
../datasets/weather_lille.parquet
../datasets/weather_limoges.parquet
../datasets/weather_lyon.parquet
../datasets/weather_marseille.parquet
../datasets/weather_nantes.parquet
../datasets/weather_paris.parquet
../datasets/weather_strasbourg.parquet
../datasets/weather_toulouse.parquet

List of 10 medium to large urban areas to approximately cover most regions in France with a slight focus on most populated regions that are likely to drive electricity demand.

city_names = [
    "paris",
    "lyon",
    "marseille",
    "toulouse",
    "lille",
    "limoges",
    "nantes",
    "strasbourg",
    "brest",
    "bayonne",
]

all_city_weather_raw = {}
for city_name in city_names:
    # all_city_weather_raw[city_name] = skrub.var(
    # f"{city_name}_weather_raw",
    all_city_weather_raw[city_name] = (
        pl.from_arrow(read_table(f"../datasets/weather_{city_name}.parquet"))
    ).with_columns(
        [
            pl.col("time").dt.cast_time_unit(
                "us"
            ),  # Ensure time column has the same type
        ]
    )

all_city_weather_raw["brest"]

shape: (38_688, 7)

time	temperature_2m	precipitation	wind_speed_10m	cloud_cover	soil_moisture_1_to_3cm	relative_humidity_2m
datetime[μs, UTC]	f32	f32	f32	f32	f32	f32
2021-01-01 00:00:00 UTC	null	null	null	null	null	null
2021-01-01 01:00:00 UTC	null	null	null	null	null	null
2021-01-01 02:00:00 UTC	null	null	null	null	null	null
2021-01-01 03:00:00 UTC	null	null	null	null	null	null
2021-01-01 04:00:00 UTC	null	null	null	null	null	null
…	…	…	…	…	…	…
2025-05-31 19:00:00 UTC	17.5175	0.0	12.0694	5.0	0.168	73.0
2025-05-31 20:00:00 UTC	16.2675	0.0	9.114471	99.0	0.168	77.0
2025-05-31 21:00:00 UTC	15.5175	0.0	7.559999	93.0	0.169	84.0
2025-05-31 22:00:00 UTC	15.5675	0.0	9.0	100.0	0.17	82.0
2025-05-31 23:00:00 UTC	15.5675	0.0	5.506941	100.0	0.171	81.0

all_city_weather_raw["brest"].drop_nulls(subset=["temperature_2m"])

shape: (36_744, 7)

time	temperature_2m	precipitation	wind_speed_10m	cloud_cover	soil_moisture_1_to_3cm	relative_humidity_2m
datetime[μs, UTC]	f32	f32	f32	f32	f32	f32
2021-03-23 00:00:00 UTC	4.628	null	10.086427	null	null	94.0
2021-03-23 01:00:00 UTC	5.028	0.0	11.183201	6.0	null	95.0
2021-03-23 02:00:00 UTC	5.078	0.0	10.966713	6.0	null	94.0
2021-03-23 03:00:00 UTC	4.628	0.0	10.464797	5.0	null	93.0
2021-03-23 04:00:00 UTC	4.428	0.0	10.464797	5.0	null	92.0
…	…	…	…	…	…	…
2025-05-31 19:00:00 UTC	17.5175	0.0	12.0694	5.0	0.168	73.0
2025-05-31 20:00:00 UTC	16.2675	0.0	9.114471	99.0	0.168	77.0
2025-05-31 21:00:00 UTC	15.5175	0.0	7.559999	93.0	0.169	84.0
2025-05-31 22:00:00 UTC	15.5675	0.0	9.0	100.0	0.17	82.0
2025-05-31 23:00:00 UTC	15.5675	0.0	5.506941	100.0	0.171	81.0

all_city_weather = time.skb.eval()
for city_name, city_weather_raw in all_city_weather_raw.items():
    all_city_weather = all_city_weather.join(
        city_weather_raw.rename(lambda x: x if x == "time" else x + "_" + city_name),
        on="time",
        how="inner",
    )

all_city_weather = skrub.var(
    "all_city_weather",
    all_city_weather,
)
all_city_weather

<Var 'all_city_weather'>

Show graph

Result:

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Calendar and holidays features

We leverage the holidays package to enrich the time range with some calendar features such as public holidays in France. We also add some features that are useful for time series forecasting such as the day of the week, the day of the year, and the hour of the day.

Note that the holidays package requires us to extract the date for the French timezone.

Similarly for the calendar features: all the time features are extracted from the time in the French timezone.

holidays_fr = holidays.country_holidays("FR", years=range(2019, 2026))

fr_time = pl.col("time").dt.convert_time_zone("Europe/Paris")
calendar = time.with_columns(
    [
        fr_time.dt.date().is_in(holidays_fr.keys()).alias("is_holiday_fr"),
        fr_time.dt.weekday().alias("day_of_week_fr"),
        fr_time.dt.ordinal_day().alias("day_of_year_fr"),
        fr_time.dt.hour().alias("hour_of_day_fr"),
    ],
)
calendar

<CallMethod 'with_columns'>

Show graph

Result:

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Electricity load data

Finally we load the electricity load data. This data will both be used as a target variable but also to craft some lagged and window-aggregated features.

load_data_files = [
    data_file
    for data_file in sorted(data_source_folder.iterdir())
    if data_file.name.startswith("Total Load - Day Ahead")
    and data_file.name.endswith(".csv")
]

electricity_raw = skrub.var(
    "electricity_raw",
    pl.concat(
        [
            pl.from_pandas(pd.read_csv(data_file, na_values=["N/A", "-"])).drop(
                ["Day-ahead Total Load Forecast [MW] - BZN|FR"]
            )
            for data_file in load_data_files
        ],
        how="vertical",
    ),
)
electricity_raw

<Var 'electricity_raw'>

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Result:

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electricity = (
    electricity_raw.with_columns(
        [
            pl.col("Time (UTC)")
            .str.split(by=" - ")
            .list.first()
            .str.to_datetime("%d.%m.%Y %H:%M", time_zone="UTC")
            .alias("time"),
        ]
    )
    .drop(["Time (UTC)"])
    .rename({"Actual Total Load [MW] - BZN|FR": "load_mw"})
    .filter(pl.col("time").dt.minute().eq(0))
    .filter(pl.col("time") >= time_range_start)
    .filter(pl.col("time") <= time_range_end)
    .select(["time", "load_mw"])
)
electricity

<CallMethod 'select'>

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Result:

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electricity.filter(pl.col("load_mw").is_null())

<CallMethod 'filter'>

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electricity.filter(
    (pl.col("time") > pl.datetime(2021, 10, 30, hour=10, time_zone="UTC"))
    & (pl.col("time") < pl.datetime(2021, 10, 31, hour=10, time_zone="UTC"))
).skb.eval().plot.line(x="time:T", y="load_mw:Q")

electricity = electricity.with_columns([pl.col("load_mw").interpolate()])
electricity.filter(
    (pl.col("time") > pl.datetime(2021, 10, 30, hour=10, time_zone="UTC"))
    & (pl.col("time") < pl.datetime(2021, 10, 31, hour=10, time_zone="UTC"))
).skb.eval().plot.line(x="time:T", y="load_mw:Q")

Check that the number of rows matches our expectations based on the number of hours that separate the first and the last dates. We can do that by joining with the time range dataframe and checking that the number of rows stays the same.

assert (
    time.join(electricity, on="time", how="inner").shape[0] == time.shape[0]
).skb.eval()

Lagged features

We can now create some lagged features from the electricity load data.

We will create 3 hourly lagged features, 1 daily lagged feature, and 1 weekly lagged feature. We will also create a rolling median and inter-quartile feature over the last 24 hours and over the last 7 days.

def iqr(col, *, window_size: int):
    """Inter-quartile range (IQR) of a column."""
    return col.rolling_quantile(0.75, window_size=window_size) - col.rolling_quantile(
        0.25, window_size=window_size
    )


electricity_lagged = electricity.with_columns(
    [pl.col("load_mw").shift(i).alias(f"load_mw_lag_{i}h") for i in range(1, 4)]
    + [
        pl.col("load_mw").shift(24).alias("load_mw_lag_1d"),
        pl.col("load_mw").shift(24 * 7).alias("load_mw_lag_1w"),
        pl.col("load_mw")
        .rolling_median(window_size=24)
        .alias("load_mw_rolling_median_24h"),
        pl.col("load_mw")
        .rolling_median(window_size=24 * 7)
        .alias("load_mw_rolling_median_7d"),
        iqr(pl.col("load_mw"), window_size=24).alias("load_mw_iqr_24h"),
        iqr(pl.col("load_mw"), window_size=24 * 7).alias("load_mw_iqr_7d"),
    ],
)
electricity_lagged

<CallMethod 'with_columns'>

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Result:

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import altair


altair.Chart(electricity_lagged.tail(100).skb.eval()).transform_fold(
    [
        "load_mw",
        "load_mw_lag_1h",
        "load_mw_lag_2h",
        "load_mw_lag_3h",
        "load_mw_lag_1d",
        "load_mw_lag_1w",
        "load_mw_rolling_median_24h",
        "load_mw_rolling_median_7d",
        "load_mw_iqr_24h",
        "load_mw_iqr_7d",
    ],
    as_=["key", "load_mw"],
).mark_line(tooltip=True).encode(x="time:T", y="load_mw:Q", color="key:N").interactive()

Investigating outliers in the lagged features

Let’s use the skrub.TableReport tool to look at the plots of the marginal distribution of the lagged features.

from skrub import TableReport

TableReport(electricity_lagged.skb.eval())

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Let’s extract the dates where the inter-quartile range of the load is greater than 15,000 MW.

electricity_lagged.filter(pl.col("load_mw_iqr_7d") > 15_000)[
    "time"
].dt.date().unique().sort().to_list().skb.eval()

[datetime.date(2021, 12, 26),
 datetime.date(2021, 12, 27),
 datetime.date(2021, 12, 28),
 datetime.date(2022, 1, 7),
 datetime.date(2022, 1, 8),
 datetime.date(2023, 1, 19),
 datetime.date(2023, 1, 20),
 datetime.date(2023, 1, 21),
 datetime.date(2024, 1, 10),
 datetime.date(2024, 1, 11),
 datetime.date(2024, 1, 12),
 datetime.date(2024, 1, 13)]

We observe 3 date ranges with high inter-quartile range. Let’s plot the electricity load and the lagged features for the first data range along with the weather data for Paris.

altair.Chart(
    electricity_lagged.filter(
        (pl.col("time") > pl.datetime(2021, 12, 1, time_zone="UTC"))
        & (pl.col("time") < pl.datetime(2021, 12, 31, time_zone="UTC"))
    ).skb.eval()
).transform_fold(
    [
        "load_mw",
        "load_mw_iqr_7d",
    ],
).mark_line(
    tooltip=True
).encode(
    x="time:T", y="value:Q", color="key:N"
).interactive()

altair.Chart(
    all_city_weather.filter(
        (pl.col("time") > pl.datetime(2021, 12, 1, time_zone="UTC"))
        & (pl.col("time") < pl.datetime(2021, 12, 31, time_zone="UTC"))
    ).skb.eval()
).transform_fold(
    [f"temperature_2m_{city_name}" for city_name in city_names],
).mark_line(
    tooltip=True
).encode(
    x="time:T", y="value:Q", color="key:N"
).interactive()

Based on the plots above, we can see that the electricity load was high just before the Christmas holidays due to low temperatures. Then the load suddenly dropped because temperatures went higher right at the start of the end-of-year holidays.

So those outliers do not seem to be caused to a data quality issue but rather due to a real change in the electricity load demand. We could conduct similar analysis for the other date ranges with high inter-quartile range but we will skip that for now.

If we had observed significant data quality issues over extended periods of time could have been addressed by removing the corresponding rows from the dataset. However, this would make the lagged and windowing feature engineering challenging to reimplement correctly. A better approach would be to keep a contiguous dataset assign 0 weights to the affected rows when fitting or evaluating the trained models via the use of the sample_weight parameter.

Final dataset

We now assemble the dataset that will be used to train and evaluate the forecasting models via backtesting.

prediction_time = time = skrub.var(
    "prediction_time",
    pl.DataFrame().with_columns(
        pl.datetime_range(
            start=time_range_start + pl.duration(days=7),
            end=time_range_end - pl.duration(hours=24),
            time_zone="UTC",
            interval="1h",
        ).alias("prediction_time"),
    ),
)
prediction_time

<Var 'prediction_time'>

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Result:

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features = (
    (
        prediction_time.join(
            electricity_lagged, left_on="prediction_time", right_on="time"
        )
        .join(all_city_weather, left_on="prediction_time", right_on="time")
        .join(calendar, left_on="prediction_time", right_on="time")
    )
    .drop("prediction_time")
    .skb.mark_as_X()
)
features

<CallMethod 'drop'>

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Result:

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horizon = 12
target_column_name = f"load_mw_horizon_{horizon}h"
predicted_target_column_name = f"predicted_{target_column_name}"

target_df = prediction_time.join(
    electricity.with_columns(
        [pl.col("load_mw").shift(-horizon).alias(target_column_name)]
    ),
    left_on="prediction_time",
    right_on="time",
)
target = target_df[target_column_name].skb.mark_as_y()

from sklearn.ensemble import HistGradientBoostingRegressor


predictions = features.skb.apply(
    HistGradientBoostingRegressor(
        random_state=0,
        learning_rate=skrub.choose_float(
            0.01, 0.9, default=0.1, log=True, name="learning_rate"
        ),
        max_leaf_nodes=skrub.choose_int(
            3, 300, default=30, log=True, name="max_leaf_nodes"
        ),
    ),
    y=target,
)
predictions

<Apply HistGradientBoostingRegressor>

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Result:

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altair.Chart(
    pl.concat(
        [
            target_df.skb.eval(),
            predictions.rename(
                {target_column_name: predicted_target_column_name}
            ).skb.eval(),
        ],
        how="horizontal",
    ).tail(24 * 7)
).transform_fold(
    [target_column_name, predicted_target_column_name],
).mark_line(
    tooltip=True
).encode(
    x="prediction_time:T", y="value:Q", color="key:N"
).interactive()

from sklearn.model_selection import TimeSeriesSplit
from sklearn.metrics import make_scorer, mean_absolute_percentage_error, get_scorer

mape_scorer = make_scorer(mean_absolute_percentage_error)

ts_cv_5 = TimeSeriesSplit(n_splits=5, max_train_size=10_000, gap=24)

predictions.skb.cross_validate(
    cv=ts_cv_5,
    scoring={
        "r2": get_scorer("r2"),
        "mape": mape_scorer,
    },
    return_train_score=True,
    verbose=1,
    n_jobs=-1,
).round(3)

[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.

[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:    5.3s finished

	fit_time	score_time	test_r2	train_r2	test_mape	train_mape
0	1.078	0.064	0.834	0.994	0.050	0.012
1	1.349	0.064	0.942	0.993	0.039	0.014
2	1.412	0.039	0.931	0.991	0.041	0.014
3	1.375	0.063	0.954	0.991	0.032	0.016
4	1.096	0.038	0.937	0.990	0.037	0.016

ts_cv_3 = TimeSeriesSplit(n_splits=3, max_train_size=10_000, gap=24)
randomized_search = predictions.skb.get_randomized_search(
    cv=ts_cv_3,
    scoring="r2",
    n_iter=30,
    fitted=True,
    verbose=1,
    n_jobs=-1,
)
randomized_search.results_

Fitting 3 folds for each of 30 candidates, totalling 90 fits

	learning_rate	max_leaf_nodes	mean_test_score
0	0.105912	37	0.946262
1	0.062359	64	0.943789
2	0.201171	17	0.943598
3	0.051621	45	0.942601
4	0.242019	55	0.941311
5	0.348018	19	0.938527
6	0.379466	9	0.937531
7	0.037431	47	0.937363
8	0.102057	221	0.936834
9	0.311903	6	0.936149
10	0.128501	220	0.935906
11	0.331880	6	0.935480
12	0.369553	10	0.935322
13	0.136961	286	0.933436
14	0.301523	24	0.933341
15	0.032321	58	0.932564
16	0.291880	4	0.931995
17	0.033058	157	0.931545
18	0.321421	3	0.923863
19	0.432172	58	0.920641
20	0.134192	3	0.909161
21	0.787469	14	0.901832
22	0.673802	100	0.895507
23	0.017316	289	0.888371
24	0.044286	5	0.887967
25	0.699648	185	0.886632
26	0.841730	177	0.868039
27	0.017173	26	0.863754
28	0.011030	30	0.770682
29	0.011831	4	0.663323

randomized_search.plot_results()

nested_cv_results = skrub.cross_validate(
    environment=predictions.skb.get_data(),
    pipeline=randomized_search,
    cv=ts_cv_5,
    scoring={
        "r2": get_scorer("r2"),
        "mape": mape_scorer,
    },
    n_jobs=-1,
    return_pipeline=True,
).round(3)

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
Cell In[32], line 1
----> 1 nested_cv_results = skrub.cross_validate(
      2     environment=predictions.skb.get_data(),
      3     pipeline=randomized_search,
      4     cv=ts_cv_5,
      5     scoring={
      6         "r2": get_scorer("r2"),
      7         "mape": mape_scorer,
      8     },
      9     n_jobs=-1,
     10     return_pipeline=True,
     11 ).round(3)

File ~/work/forecasting/forecasting/.pixi/envs/doc/lib/python3.12/site-packages/skrub/_expressions/_estimator.py:608, in cross_validate(pipeline, environment, keep_subsampling, **kwargs)
    606 kwargs = _rename_cv_param_pipeline_to_estimator(kwargs)
    607 X, y = _compute_Xy(pipeline.expr, environment)
--> 608 result = model_selection.cross_validate(
    609     _to_Xy_pipeline(pipeline, environment),
    610     X,
    611     y,
    612     **kwargs,
    613 )
    614 if (fitted_pipelines := result.pop("estimator", None)) is not None:
    615     result["pipeline"] = [_to_env_pipeline(p) for p in fitted_pipelines]

File ~/work/forecasting/forecasting/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/utils/_param_validation.py:218, in validate_params.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
    212 try:
    213     with config_context(
    214         skip_parameter_validation=(
    215             prefer_skip_nested_validation or global_skip_validation
    216         )
    217     ):
--> 218         return func(*args, **kwargs)
    219 except InvalidParameterError as e:
    220     # When the function is just a wrapper around an estimator, we allow
    221     # the function to delegate validation to the estimator, but we replace
    222     # the name of the estimator by the name of the function in the error
    223     # message to avoid confusion.
    224     msg = re.sub(
    225         r"parameter of \w+ must be",
    226         f"parameter of {func.__qualname__} must be",
    227         str(e),
    228     )

File ~/work/forecasting/forecasting/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:399, in cross_validate(estimator, X, y, groups, scoring, cv, n_jobs, verbose, params, pre_dispatch, return_train_score, return_estimator, return_indices, error_score)
    396 # We clone the estimator to make sure that all the folds are
    397 # independent, and that it is pickle-able.
    398 parallel = Parallel(n_jobs=n_jobs, verbose=verbose, pre_dispatch=pre_dispatch)
--> 399 results = parallel(
    400     delayed(_fit_and_score)(
    401         clone(estimator),
    402         X,
    403         y,
    404         scorer=scorers,
    405         train=train,
    406         test=test,
    407         verbose=verbose,
    408         parameters=None,
    409         fit_params=routed_params.estimator.fit,
    410         score_params=routed_params.scorer.score,
    411         return_train_score=return_train_score,
    412         return_times=True,
    413         return_estimator=return_estimator,
    414         error_score=error_score,
    415     )
    416     for train, test in indices
    417 )
    419 _warn_or_raise_about_fit_failures(results, error_score)
    421 # For callable scoring, the return type is only know after calling. If the
    422 # return type is a dictionary, the error scores can now be inserted with
    423 # the correct key.

File ~/work/forecasting/forecasting/.pixi/envs/doc/lib/python3.12/site-packages/sklearn/utils/parallel.py:82, in Parallel.__call__(self, iterable)
     73 warning_filters = warnings.filters
     74 iterable_with_config_and_warning_filters = (
     75     (
     76         _with_config_and_warning_filters(delayed_func, config, warning_filters),
   (...)     80     for delayed_func, args, kwargs in iterable
     81 )
---> 82 return super().__call__(iterable_with_config_and_warning_filters)

File ~/work/forecasting/forecasting/.pixi/envs/doc/lib/python3.12/site-packages/joblib/parallel.py:2072, in Parallel.__call__(self, iterable)
   2066 # The first item from the output is blank, but it makes the interpreter
   2067 # progress until it enters the Try/Except block of the generator and
   2068 # reaches the first `yield` statement. This starts the asynchronous
   2069 # dispatch of the tasks to the workers.
   2070 next(output)
-> 2072 return output if self.return_generator else list(output)

File ~/work/forecasting/forecasting/.pixi/envs/doc/lib/python3.12/site-packages/joblib/parallel.py:1682, in Parallel._get_outputs(self, iterator, pre_dispatch)
   1679     yield
   1681     with self._backend.retrieval_context():
-> 1682         yield from self._retrieve()
   1684 except GeneratorExit:
   1685     # The generator has been garbage collected before being fully
   1686     # consumed. This aborts the remaining tasks if possible and warn
   1687     # the user if necessary.
   1688     self._exception = True

File ~/work/forecasting/forecasting/.pixi/envs/doc/lib/python3.12/site-packages/joblib/parallel.py:1800, in Parallel._retrieve(self)
   1789 if self.return_ordered:
   1790     # Case ordered: wait for completion (or error) of the next job
   1791     # that have been dispatched and not retrieved yet. If no job
   (...)   1795     # control only have to be done on the amount of time the next
   1796     # dispatched job is pending.
   1797     if (nb_jobs == 0) or (
   1798         self._jobs[0].get_status(timeout=self.timeout) == TASK_PENDING
   1799     ):
-> 1800         time.sleep(0.01)
   1801         continue
   1803 elif nb_jobs == 0:
   1804     # Case unordered: jobs are added to the list of jobs to
   1805     # retrieve `self._jobs` only once completed or in error, which
   (...)   1811     # timeouts before any other dispatched job has completed and
   1812     # been added to `self._jobs` to be retrieved.

KeyboardInterrupt: 

nested_cv_results

for outer_cv_idx in range(len(nested_cv_results)):
    print(nested_cv_results.loc[outer_cv_idx, "pipeline"].results_.loc[0].round(3).to_dict())

# from joblib import Parallel, delayed

# cv_predictions = []
# for ts_cv_train_idx, ts_cv_test_idx in ts_cv_5.split(prediction_time.skb.eval()):
#     features[ts_cv_train_idx].fit